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Genetic diversity and population structureof the endangered basal angiospermBrasenia schreberi (Cabombaceae) in ChinaZhi-Zhong Li1,2, Andrew W. Gichira1,2,3, Qing-Feng Wang2,3and Jin-Ming Chen21University of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden,Chinese Academy of Sciences, Wuhan, China3Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan, China2ABSTRACTSubmitted 31 March 2018Accepted 3 July 2018Published 13 July 2018Brasenia schreberi J.F. Gmelin (Cabombaceae), an aquatic herb that occurs infragmented locations in China, is rare and endangered. Understanding its geneticdiversity and structure is crucial for its conservation and management. In this study,12 microsatellite markers were used to estimate the genetic diversity and variation in21 populations of B. schreberi in China. A total of 61 alleles were found; assessmentof allelic richness (Ar 1.92) and observed and expected heterozygosity(HO 0.200, HE 0.256) suggest lower genetic diversity compared to someendangered species, and higher variation was observed within populations (58.68%)rather than among populations (41.32%). No significant correlation betweengeographical and genetic distance among populations was detected (Mantel test,r 0.0694; P 0.7985), which may have likely resulted from barriers to gene flow(Nm 0.361) that were produced by habitat fragmentation. However, Bayesian andneighbor-joining cluster analyses suggest a population genetic structure consistingof two clusters (I and II) or four subclusters (I-1, 2 and II-1, 2). The genetic structureand distribution of B. schreberi in China may have involved glacial refugia thatunderwent range expansions, introgression, and habitat fragmentation. The findingsof the present study emphasize the importance for both in situ and ex situconservation efforts.Corresponding authorsZhi-Zhong Li,wbg georgelee@163.comJin-Ming Chen, jmchen@wbgcas.cnSubjects Biodiversity, Evolutionary Studies, Genetics, Genomics, Plant ScienceKeywords Basal angiosperm, Brasenia schreberi, Endangered, Genetic structure, MicrosatelliteAcademic editorAlastair CulhamINTRODUCTIONAdditional Information andDeclarations can be found onpage 14DOI 10.7717/peerj.5296Copyright2018 Li et al.Distributed underCreative Commons CC-BY 4.0Brasenia schreberi J.F. Gmelin, a basal angiosperm that belongs to family Cabombaceae(Nymphaeales), is a perennial aquatic plant. Similar to most aquatic plants, B. schreberican reproduce sexually by outcrossing or asexually through rhizomes and winter buds(Bertin, 1993; Griffin, Mavraganis & Eckert, 2000). It has a wide yet and sporadicgeographical distribution in temperate and tropical regions of Asia, Australia, Africa,India, and North and South America (Kim et al., 2012).Prior to the early 20th century, B. schreberi was widely distributed in China and grew inunpolluted aquatic environments such as freshwater ponds, lakes, swamps, and evenHow to cite this article Li et al. (2018), Genetic diversity and population structure of the endangered basal angiosperm Brasenia schreberi(Cabombaceae) in China. PeerJ 6:e5296; DOI 10.7717/peerj.5296
agricultural fields. However, in recent decades, its population has significantlydecreased due to the loss of natural habitats and deterioration in water qualityresulting from excessive human activities, particularly involving leaves harvested forfood and increasing the use of fertilizers and pesticides. Previous field investigationsexperienced difficulty in finding natural populations in regions within China whereB. schreberi was previously known to grow in abundance (Gao, Zhang & Chen, 2007).Similar situations have been reported in other countries such as Korea and Japan(Kim et al., 2012). It is currently listed as a critically endangered species in China,belonging to the first category of key protected wild plants (Yu, 1999). Therefore,effective conservation management to preserve the remaining populations ofB. schreberi is imperative.Demonstration of genetic diversity and structure in rare plant species is often crucialfor formulation of conservation and management strategies because it providesvaluable insights into the vital aspects of demography, reproduction, and ecology(Zaya et al., 2017). Previous studies have employed various molecular markers to assessthe genetic diversity of B. schreberi, including inter-simple sequence repeat markers(Zhang & Gao, 2008), randomly amplified polymorphic DNA, amplified fragments lengthpolymorphisms (Kim, Na & Choi, 2008), and nuclear ribosomal spacer and chloroplastDNA sequences (Kim et al., 2012). Despite the significant decrease in the size ofB. schreberi populations, these studies reveal significant genetic diversity, highlightfactors that negatively influence genetic diversity, and propose potential conservationmeasures. Zhang & Gao (2008) investigated the population diversity in semi-naturalpopulations of B. schreberi in Zhejiang and Suzhou provinces, whereas Kim et al. (2012)focused on natural populations from South Korea. However, no research investigations onthe level and pattern of diversity and genetic structure in the wild populations ofB. schreberi in China have been conducted to date.Compared to other molecular markers, simple sequence repeat (SSR) markershave numerous merits, including co-dominance, high reproducibility, a relatively highlevel of polymorphisms, and are plentiful in the genome (Liao et al., 2013). SSR markershave been successfully applied to estimate the genetic diversity of other aquaticplants such as Sparganium emersum (Pollux et al., 2007), Zostera marina (Reusch,Stam & Olsen, 2000; Talbot et al., 2016), Sagittaria natans (Yue et al., 2011), Zizanialatifolia (Chen et al., 2012, 2017), Nymphoides peltata (Liao et al., 2013), Isoeteshypsophila (Li et al., 2013), Ruppia cirrhosa (Martı́nez-Garrido, González-Wangüemert &Serrão, 2014), Nuphar submersa (Shiga et al., 2017), and Ottelia acuminata (Zhai, Yin &Yang, 2018). Here, we used 12 microsatellite loci to detect and estimate the geneticvariation of B. schreberi in China. In this study, a total of 21 populations, representingnearly the entire known natural distribution zones in China, were sampled. We aimedto determine (1) how the extent of genetic diversity is apportioned within andamong populations of B. schreberi, and (2) the genetic structure and its associationwith geographical distribution. The findings of the present study may be utilized inconservation efforts on this species.Li et al. (2018), PeerJ, DOI 10.7717/peerj.52962/18
Figure 1 Genetic structure of B. schreberi populations in China. (A) Sampling area and genetic structure of B. schreberi populations based on K 2 genetic clusters (I and II); (B) proportional membership of 21 B. schreberi populations to K 2 and 4 (subcluster I-1, I-2, II-1, II-2) geneticclusters. Individuals are represented by a single vertical column divided into two or four ( K) colors. The relative length of the colored segmentcorresponds to the individual’s estimated proportion of membership in that cluster. Map data 2016 Google.Full-size DOI: 10.7717/peerj.5296/fig-1MATERIALS AND METHODSSample collectionDuring June–September 2016, fresh leaves of 376 individuals from 21 locations acrossalmost the entire geographical distribution of B. schreberi in China were collected (Fig. 1)and rapidly dried in silica gel until further analyses. We sampled a range of 14–20individuals per site (Table 1). Because B. schreberi could reproduce asexually throughLi et al. (2018), PeerJ, DOI 10.7717/peerj.52963/18
Table 1 Location of populations and number of samples in each population of B. schreberi in China.PopulationHZXHLocationHangzhou, ZhejiangLatitude/LongitudeSample sizeVoucher code 14HIB-BS08 30 08′N/120 13′EXGLHTGanzhou, Jiangxi26 04′N/115 21′E16HIB-BS06CQSZShizhu, Chongqing30 09′N/108 29′E16HIB-BS01 HZTJHHangzhou, Zhejiang30 07′N/120 04′E16HIB-BS02NXQZSNanxiong, Guangdong25 03′N/114 25′E15HIB-BS11 SZDSZSuzhou, Jiangsu31 04′N/120 24′E16HIB-BS15SZLTSSuzhou, Jiangsu31 03′N/120 24′E15HIB-BS10 YNGLGGaoligong, Yunnan28 01′N/98 38′E20HIB-BS18YNTCTengchong, Yunnan25 44′N/98 34′E14HIB-BS19 MSGHMangshan, Hunan25 03′N/113 00′E20HIB-BS12YTLHSYingtan, Jiangxi28 04′N/117 02′E20HIB-BS22 LBMHLeibo, Sichuan28 26′N/103 48′E20HIB-BS26CLHLChaling, Hunan26 50′N/113 40′E19HIB-BS29 GDSYDGuidong, Zhejiang26 04′N/114 01′E17HIB-BS36QYBSZQingyuan, Zhejiang27 43′N/119 11′E20HIB-BS23QYSYHQingyuan, Zhejiang 20HIB-BS07 27 32′N/119 19′ESCHSYSuichang, Zhejiang28 28′N/118 51′E20HIB-BS09NDXFSNingde, Fujian27 09′N/119 15′E20HIB-BS14 ZYFSZongyang, Anhui30 55′N/117 17′E20HIB-BS17LCFBSLichuan, Hubei30 12′N/108 42′E18HIB-BS2020HIB-BSTWTWYLYiliang, Taiwan 24 38′N/121 31′ETotal376its stolons, we selected leaves from plants within populations that were separated by atleast five metres to reduce the collection of clonal individuals. Voucher specimens fromeach population were deposited in the herbarium of Wuhan Botanical Garden, ChineseAcademy of Sciences.DNA extraction, amplification, and sequencingTotal genomic DNA was extracted from silica-dried leaves using the modifiedcetyltrimethylammonium bromide method described by Doyle & Doyle (1987). SSRs weregenotyped using 12 polymorphic SSR loci from previous study (Liu et al., 2016) usingpolymerase chain reaction (PCR). The total reaction volume was 25 mL, which consistedof 0.25 mM of each dNTP, five mL of 10 Taq buffer (10 mM Tris–HCl, (pH 8.3), 1.5 mMMgCl2, and 50 mM KCl), one mM of each forward primer labeled with a fluorescentchemical and an unlabeled reverse primer, one U of Taq polymerase (TransGen BiotechCo., Beijing, China), and 10–30 ng of genomic DNA as template. PCR amplificationwas performed with a PTC-100 thermocycler (Bio-Rad, Hercules, CA, USA) using thefollowing program profile: 95 C for 3 min; followed by 30 cycles of 95 C for 30 s,annealing at 55–60 C for 30 s (depending on primer), and 72 C for 40 s; and a finalextension at 72 C for 10 min. The PCR products were separated via electrophoresis onLi et al. (2018), PeerJ, DOI 10.7717/peerj.52964/18
1.0% (w/v) agarose gels, stained with ethidium bromide, and observed under UV light.Then, the multiplex amplified PCR products were sequenced on an ABI prism3730xl and sized using an internal DNA standard (Rox-500; Life Technologies,Shanghai, China). The SSR fragments were visualized with GENEMAPPER v.4.0(Applied Biosystems, Foster City, CA, USA).Data analysisThe loci were tested for Hardy–Weinberg equilibrium (HWE) and linkage disequilibriumusing GENEPOP ver. 4.0 (Rousset, 2008). Deviations from HWE due to null alleles,stuttering, and large allele dropout were assessed using MICROCHECKER ver. 2.2.3(Van Oosterhout et al., 2004).The total number of alleles (Na), the effective number of alleles (Ne), observed (Ho)and expected (He) heterozygosity, Shannon’s information index (I), F-statistics (FIS, FST),and gene flow (Nm) were computed for each locus using POPGENE v.1.31 (Yeh, Yang &Boyle, 1999). The Polymorphic Information Content (PIC) for each locus wasestimated using CERVUS ver. 3.0 (Kalinowski, Taper & Marshall, 2007).For each population, the average number of alleles (NA), NE, the number of privateallele (AP), HO, HE, I and, the pairwise FST between each pair of populations wereestimated using GENALEX ver. 6.0 (Peakall & Smouse, 2006). Allelic richness (Ar)and inbreeding coefficients (FIS) were calculated using the diveRsity (Keenan et al., 2013)packages and confidence intervals were estimated with 10,000 bootstrap replicates.Gene flow (Nm) among populations was calculated using the expression Nm (1 - FST)/4FST(Slatkin, 1993). Analysis of molecular variation was used to evaluate the relative levelof genetic variations among groups (FCT), among populations within groups (FSC),and among individuals within populations (FST); these values, and the significance ofeach value, were tested using Arlequin ver. 3.5 (Excoffier, Smouse & Quattro, 1992;Excoffier, Laval & Schneider, 2005).The neighbor-joining (NJ) tree was constructed using the software TreeFit (Kalinowski,2009) based on Nei’s genetic distance (DA; Nei, 1987) to reveal the genetic relationshipsamong the populations used in this study. The correlation between genetic distance andgeographic distance was estimated using a Mantel test based on a matrix of geneticdistance using DA and a pairwise matrix of geographic distance. Isolation by Distance WebService ver. 3.23 (Jensen, Bohonak & Kelley, 2005) was used to test for significance with10,000 permutations.The BOTTLENECK program (Piry, Luikart & Cornuet, 1999) was used to estimate thepossible influence of recent demographic changes on genetic diversity and identifygenetic bottlenecks among populations. Wilcoxon signed-rank tests were conducted usingthree different models with 10,000 replicates: the infinite allele mutation model (IAM),the stepwise mutation model (SMM), the two-phased model of mutation (TPM)which with 70% of mutations were assumed to occur under the SMM, and 30% wereassumed to occur under the IAM. The mode shift of each population was also estimatedusing BOTTLENECK using default settings.Li et al. (2018), PeerJ, DOI 10.7717/peerj.52965/18
Table 2 Genetic diversity at 12 SSR loci in 376 individuals of B. schreberi.FISFSTNmLocusAnnealingtemp ( C)Allelesize 70.5230.1290.2710.2390.1690.0530.132Note:Na, the total number of alleles; Ne, the effective number of alleles; Ho, the observed heterozygosity; He, the expectedheterozygosity; I, Shannon’s information index; FIS, coefficient of inbreeding; FST, genetic differentiation coefficient;PIC, polymorphic information content; Nm, gene flow.The population structure was tested using STRUCTURE ver. 2.3.1 (Pritchard, Stephens &Donnelly, 2000) based on a Bayesian clustering method. The approach was used tocluster genetically similar individuals and assess the most likely clustering state underthe Hardy–Weinberg principle. The optimum number of genetic clusters (K) was testedfrom K 2 to 20 clusters based on assuming admixture, correlated allele frequencies.The analysis was performed for 10 iterations, with a burn-in of 80,000 replications,followed by 800,000 Markov Chain Monte Carlo replications. The most likely number ofclusters was verified based on the K method (Evanno, Regnaut & Goudet, 2005).To generate a consensus K, independent runs of all data were normalized with CLUMPPver. 1.1.2 (Jakobsson & Rosenberg, 2007) using the Greedy algorithm with 100,000 repeats.DISTRUCT ver. 1.1 (Rosenberg, 2004) was used to visualize the population structure.RESULTSMicrosatellite variation and genetic diversity within populationsNull alleles were observed in a few loci (BS02, BS06, BS09, BS18, and BS19). A total of 61alleles were detected across 12 SSR loci in 376 individuals from 21 populations (Table 2).The number of alleles generated by each marker ranged from two at locus BS18 to 13at locus BS05, with an average of 5,167 alleles at each locus. Ne for each locus rangedfrom 1.075 to 5.710. Ho ranged from 0.003 to 0.955, and He ranged from 0.07 to 0.826.High FST values (FST 0.152–0.710, mean 0.393) and PIC values (PIC 0.483–0.854,mean 0.591) were detected at all loci. The average FIS across all loci was 0.341.Nm per locus varied between 0.102 in locus BS09 and 1.393 in locus BS18 (Table 2).Li et al. (2018), PeerJ, DOI 10.7717/peerj.52966/18
Table 3 Summary statistics for B. schreberi populations in Number in parentheses are standard deviations.NA, the average number of alleles; NE, the effective number of alleles; Ar, the allelic richness; Ap, the private allele; HO, the observed heterozygosity; HE, the expectedheterozygosity; PPL, percentage of polymorphic loci; FIS, the inbreeding coefficient; I, Shannon’s information index.*/** Significant difference (P 0.1/0.05).Li et al. (2018), PeerJ, DOI 10.7717/peerj.52967/18
The average number of alleles per population ranged from 1.5 0.151 (QYSYH) to2.667 0.284 (YTLHS). Six private alleles, distinctive to a specific population, wereobserved in population YTLHS, three were recorded in population LBMH, and twowere recorded in population GDSYD, whereas other populations, including SZDSZ,SZLTS, YNTC, MSGH, CLHL, and TWYL, had a single private allele each (Table 3).Ar ranged from 1.49 to 2.58. HO and HE ranged from 0.093 0.083 to 0.354 0.137and from 0.139 0.056 to 0.398 0.051, respectively. I ranged from 0.220 0.081 to0.660 0.091. FIS ranged from -0.349 to 0.666.The Wilcoxon signed-rank tests revealed significant bottlenecks in 11 populationsbased on the IAM, SMM, and TPM assumptions, and normal models o
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